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api/ltx/ltx_aduc_manager.py

@@ -27,24 +27,12 @@ LTX_VIDEO_REPO_DIR = Path("/data/LTX-Video")
 LTX_REPO_ID = "Lightricks/LTX-Video"
 CACHE_DIR = os.environ.get("HF_HOME")
 
-
-def add_deps_to_path():
-    """
-    Adiciona o diretório do repositório LTX ao sys.path para garantir que suas
-    bibliotecas possam ser importadas.
-    """
-    repo_path = str(LTX_VIDEO_REPO_DIR.resolve())
-    if repo_path not in sys.path:
-        sys.path.insert(0, repo_path)
-        logging.info(f"[ltx_utils] LTX-Video repository added to sys.path: {repo_path}")
-
-# Executa a função imediatamente para configurar o ambiente antes de qualquer importação.
-add_deps_to_path()
-
 # --- Importações da biblioteca LTX-Video ---
-from ltx_video.pipelines.pipeline_ltx_video import LTXVideoPipeline
-from ltx_video.models.autoencoders.vae_encode import vae_encode, latent_to_pixel_coords
-
+repo_path = str(LTX_VIDEO_REPO_DIR.resolve())
+if repo_path not in sys.path:
+   sys.path.insert(0, repo_path)
+   from ltx_video.pipelines.pipeline_ltx_video import LTXVideoPipeline
+   from ltx_video.models.autoencoders.vae_encode import vae_encode, latent_to_pixel_coords
 
 # ==============================================================================
 # --- DEFINIÇÃO DOS DATACLASSES DE CONDICIONAMENTO ADUC-SDR ---
@@ -142,22 +130,19 @@ class LTXWorker:
         self.vae_device = torch.device(vae_device_str)
         self.config = config
         self.pipeline: LTXVideoPipeline = None
-        
         self._load_and_patch_pipeline()
 
     def _load_and_patch_pipeline(self):
         logging.info(f"[LTXWorker-{self.main_device}] Carregando pipeline LTX para a CPU...")
         self.pipeline, _ = build_ltx_pipeline_on_cpu(self.config)
-        
         logging.info(f"[LTXWorker-{self.main_device}] Movendo pipeline para GPUs (Main: {self.main_device}, VAE: {self.vae_device})...")
         self.pipeline.to(self.main_device)
         self.pipeline.vae.to(self.vae_device)
-
         logging.info(f"[LTXWorker-{self.main_device}] Aplicando patch ADUC-SDR na função 'prepare_conditioning'...")
         self.pipeline.prepare_conditioning = _aduc_prepare_conditioning_patch.__get__(self.pipeline, LTXVideoPipeline)
         logging.info(f"[LTXWorker-{self.main_device}] ✅ Pipeline 'quente', corrigido e pronto para uso.")
 
-class LTXPoolManager:
+class LtxAducManager:
     _instance = None
     _lock = threading.Lock()
 
@@ -172,15 +157,11 @@ class LTXPoolManager:
         if self._initialized: return
         with self._lock:
             if self._initialized: return
-            
             logging.info("⚙️ Inicializando LTXPoolManager Singleton...")
             self.config = self._load_config()
-
             main_device_str = str(gpu_manager.get_ltx_device())
             vae_device_str = str(gpu_manager.get_ltx_vae_device())
-            
             self.worker = LTXWorker(main_device_str, vae_device_str, self.config)
-            
             self._initialized = True
             logging.info("✅ LTXPoolManager pronto.")
 
@@ -190,11 +171,9 @@ class LTXPoolManager:
         with open(config_path, "r") as file:
             return yaml.safe_load(file)
 
-    
-
     def get_pipeline(self) -> LTXVideoPipeline:
         """Retorna a instância do pipeline, já carregada e corrigida."""
         return self.worker.pipeline
 
 # --- Instância Singleton Global ---
-ltx_pool_manager = LTXPoolManager()
+ltx_aduc_manager = LtxAducManager()

api/ltx/ltx_aduc_pipeline.py

@@ -18,6 +18,13 @@ import torch
 import yaml
 import numpy as np
 from PIL import Image
+from api.ltx.ltx_utils import seed_everything
+from utils.debug_utils import log_function_io
+from managers.gpu_manager import gpu_manager
+from api.ltx.ltx_aduc_manager import ltx_aduc_manager, LatentConditioningItem
+from api.ltx.vae_aduc_pipeline import vae_aduc_pipeline
+from tools.video_encode_tool import video_encode_tool_singleton
+
 
 # ==============================================================================
 # --- SETUP E IMPORTAÇÕES DO PROJETO ---
@@ -27,7 +34,7 @@ from PIL import Image
 import warnings
 warnings.filterwarnings("ignore")
 logging.getLogger("huggingface_hub").setLevel(logging.ERROR)
-log_level = os.environ.get("ADUC_LOG_LEVEL", "INFO").upper()
+log_level = logging.DEBUG
 logging.basicConfig(level=log_level, format='[%(levelname)s] [%(name)s] %(message)s')
 
 # --- Constantes de Configuração ---
@@ -37,29 +44,16 @@ RESULTS_DIR = Path("/app/output")
 DEFAULT_FPS = 24.0
 FRAMES_ALIGNMENT = 8
 
-from api.ltx.ltx_utils import seed_everything
-from utils.debug_utils import log_function_io
-
-
 # Garante que a biblioteca LTX-Video seja importável
-def add_deps_to_path():
-    repo_path = str(LTX_VIDEO_REPO_DIR.resolve())
-    if repo_path not in sys.path:
-        sys.path.insert(0, repo_path)
-add_deps_to_path()
-
-# --- Módulos da nossa Arquitetura ---
-from managers.gpu_manager import gpu_manager
-from api.ltx.ltx_aduc_manager import ltx_pool_manager, LatentConditioningItem
-from api.ltx.vae_aduc_pipeline import vae_server_singleton
-from tools.video_encode_tool import video_encode_tool_singleton
-
+repo_path = str(LTX_VIDEO_REPO_DIR.resolve())
+if repo_path not in sys.path:
+    sys.path.insert(0, repo_path)
 
 # ==============================================================================
 # --- CLASSE DE SERVIÇO (O ORQUESTRADOR) ---
 # ==============================================================================
 
-class VideoService:
+class LtxAducPipeline:
     """
     Orchestrates the high-level logic of video generation, delegating all
     low-level tasks to specialized managers and utility modules.
@@ -70,13 +64,13 @@ class VideoService:
         t0 = time.time()
         logging.info("Initializing VideoService Orchestrator...")
         
-        if ltx_pool_manager is None or vae_server_singleton is None:
+        if ltx_aduc_manager is None or vae_aduc_pipeline is None:
             raise RuntimeError("A required manager (LTX or VAE) failed to initialize. Aborting.")
 
-        self.pipeline = ltx_pool_manager.get_pipeline()
+        self.pipeline = ltx_aduc_manager.get_pipeline()
         self.main_device = self.pipeline.device
         self.vae_device = self.pipeline.vae.device
-        self.config = ltx_pool_manager.config
+        self.config = ltx_aduc_manager.config
         
         self._apply_precision_policy()
         logging.info(f"VideoService ready. Using Main: {self.main_device}, VAE: {self.vae_device}. Startup time: {time.time() - t0:.2f}s")
@@ -121,7 +115,7 @@ class VideoService:
         initial_conditions = []
         if initial_media_items:
             logging.info("Delegating to VaeServer to prepare initial conditioning items...")
-            initial_conditions = vae_server_singleton.generate_conditioning_items(
+            initial_conditions = vae_aduc_pipeline.generate_conditioning_items(
                 media_items=[item[0] for item in initial_media_items],
                 target_frames=[item[1] for item in initial_media_items],
                 strengths=[item[2] for item in initial_media_items],
@@ -278,7 +272,7 @@ class VideoService:
         torch.save(final_latents, final_latents_path)
         logging.info(f"Final latents saved to: {final_latents_path}")
         
-        pixel_tensor = vae_server_singleton.decode_to_pixels(
+        pixel_tensor = vae_aduc_pipeline.decode_to_pixels(
             final_latents, decode_timestep=float(self.config.get("decode_timestep", 0.05))
         )
         video_path = self._save_and_log_video(pixel_tensor, f"{base_filename}_{seed}")
@@ -293,7 +287,6 @@ class VideoService:
                 config_dict[key] = ui_value
                 logging.info(f"Override: '{key}' set to {ui_value} by UI.")
   
-
     def _save_and_log_video(self, pixel_tensor: torch.Tensor, base_filename: str) -> Path:
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_path = os.path.join(temp_dir, f"{base_filename}.mp4")
@@ -326,5 +319,5 @@ class VideoService:
 # ==============================================================================
 # --- INSTANCIAÇÃO SINGLETON ---
 # ==============================================================================
-video_generation_service = VideoService()
-logging.info("Global VideoService orchestrator instance created successfully.")
+ltx_aduc_pipeline = LtxAducPipeline()
+logging.info("Global VideoService orchestrator instance created successfully.")

api/ltx/ltx_utils.py

@@ -11,7 +11,6 @@ import sys
 from pathlib import Path
 from typing import Dict, Optional, Tuple, Union
 from huggingface_hub import hf_hub_download
-
 import numpy as np
 import torch
 import torchvision.transforms.functional as TVF
@@ -28,24 +27,13 @@ LTX_VIDEO_REPO_DIR = Path("/data/LTX-Video")
 LTX_REPO_ID = "Lightricks/LTX-Video"
 CACHE_DIR = os.environ.get("HF_HOME")
 
-def add_deps_to_path():
-    """
-    Adiciona o diretório do repositório LTX ao sys.path para garantir que suas
-    bibliotecas possam ser importadas.
-    """
-    repo_path = str(LTX_VIDEO_REPO_DIR.resolve())
-    if repo_path not in sys.path:
-        sys.path.insert(0, repo_path)
-        logging.info(f"[ltx_utils] LTX-Video repository added to sys.path: {repo_path}")
-
-# Executa a função imediatamente para configurar o ambiente antes de qualquer importação.
-add_deps_to_path()
-
-
 # ==============================================================================
 # --- IMPORTAÇÕES DA BIBLIOTECA LTX-VIDEO (Após configuração do path) ---
 # ==============================================================================
-try:
+
+repo_path = str(LTX_VIDEO_REPO_DIR.resolve())
+if repo_path not in sys.path:
+    sys.path.insert(0, repo_path)
     from ltx_video.pipelines.pipeline_ltx_video import LTXVideoPipeline
     from ltx_video.models.autoencoders.latent_upsampler import LatentUpsampler
     from ltx_video.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
@@ -53,9 +41,6 @@ try:
     from ltx_video.models.transformers.symmetric_patchifier import SymmetricPatchifier
     from ltx_video.schedulers.rf import RectifiedFlowScheduler
     import ltx_video.pipelines.crf_compressor as crf_compressor
-except ImportError as e:
-    raise ImportError(f"Could not import from LTX-Video library even after setting sys.path. Check repo integrity at '{LTX_VIDEO_REPO_DIR}'. Error: {e}")
-
 
 # ==============================================================================
 # --- FUNÇÕES DE CONSTRUÇÃO DE MODELO E PIPELINE ---
@@ -121,7 +106,6 @@ def build_ltx_pipeline_on_cpu(config: Dict) -> Tuple[LTXVideoPipeline, Optional[
     logging.info(f"LTX pipeline built on CPU in {time.perf_counter() - t0:.2f}s")
     return pipeline, latent_upsampler
 
-
 # ==============================================================================
 # --- FUNÇÕES AUXILIARES (Seed, Preparação de Imagem) ---
 # ==============================================================================

api/ltx/vae_aduc_pipeline.py

@@ -14,28 +14,26 @@ import yaml
 import torch
 import numpy as np
 from PIL import Image
+from api.ltx.ltx_aduc_manager import LatentConditioningItem
+from managers.gpu_manager import gpu_manager
+from api.ltx.ltx_aduc_manager import ltx_aduc_manager
 
 # ==============================================================================
 # --- IMPORTAÇÕES DA ARQUITETURA E DO LTX ---
 # ==============================================================================
-from api.ltx.ltx_aduc_manager import LatentConditioningItem
-from managers.gpu_manager import gpu_manager
-from api.ltx.ltx_aduc_manager import ltx_pool_manager
 
 # Adiciona o path para as bibliotecas do LTX
-
 LTX_VIDEO_REPO_DIR = Path("/data/LTX-Video")
 if str(LTX_VIDEO_REPO_DIR.resolve()) not in sys.path:
     sys.path.insert(0, str(LTX_VIDEO_REPO_DIR.resolve()))
-    
-from ltx_video.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
-from ltx_video.models.autoencoders.vae_encode import vae_encode, vae_decode
+    from ltx_video.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
+    from ltx_video.models.autoencoders.vae_encode import vae_encode, vae_decode
 
 # ==============================================================================
 # --- CLASSE DO SERVIÇO VAE ---
 # ==============================================================================
 
-class VaeServer:
+class VaeAducPipeline:
     _instance = None
     _lock = threading.Lock()
 
@@ -60,10 +58,10 @@ class VaeServer:
             # 2. Obter o modelo VAE já carregado pelo LTXPoolManager
             #    Isso garante consistência e evita carregar o modelo duas vezes.
             try:
-                from api.ltx.ltx_aduc_manager import ltx_pool_manager
-                if ltx_pool_manager is None or ltx_pool_manager.get_pipeline() is None:
+                from api.ltx.ltx_aduc_manager import ltx_aduc_manager
+                if ltx_aduc_manager is None or ltx_aduc_manager.get_pipeline() is None:
                     raise RuntimeError("LTXPoolManager is not initialized yet. VaeServer must be initialized after.")
-                self.vae = ltx_pool_manager.get_pipeline().vae
+                self.vae = ltx_aduc_manager.get_pipeline().vae
             except Exception as e:
                 logging.critical(f"Failed to get VAE from LTXPoolManager. Error: {e}", exc_info=True)
                 raise
@@ -150,5 +148,4 @@ class VaeServer:
         finally:
             self._cleanup_gpu()
 
-
-vae_server_singleton = VaeServer()
+vae_aduc_pipeline = VaeAducPipeline()